Directed trajectories through communication decision tree using iterative artificial intelligence

ABSTRACT

Embodiments relate to configuring artificial-intelligence (AI) decision nodes throughout a communication decision tree. The decision nodes can support successive iteration of AI models to dynamically define iteration data that corresponds to a trajectory through the tree

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of and the priority to U.S.Provisional Application No. 62/566,026, filed on Sep. 29, 2017, which ishereby incorporated by reference in its entirety for all purposes.

FIELD

Embodiments relate to configuring artificial-intelligence (AI) decisionnodes throughout a communication decision tree. The decision nodes cansupport successive iteration of AI models to dynamically defineiteration data that corresponds to a trajectory through the tree.

BACKGROUND

Technological advancements have improved the accessibility andcomplexity of multiple types of communication channels. Further,data-storage and network advancements have increased capacities, suchthat an increasing amount (and variety) of data can be stored at a datasource for potential transmission. Therefore, a data source can bepositioned to deliver many types of data across any of multiple datachannels at many potential times. The array of content-delivery optionsexplodes when considering multiple, related content deliveries insteadof a single distribution. Frequently, a content provider configures oneor more static rules to indiscriminately provide the same contentthrough a same communication channel to each data ingester. While thecommunication specification(s) may differ across receipt of differentdata requests, the rule(s) can be configured to indiscriminately andconsistently respond to data requests. Though this approach providesconfiguration simplicity and deterministic operation, it fails to reactto the potential variability across a population of data ingesters andthus may sub-optimally handle requests.

SUMMARY

In some embodiments, a computer-implemented method is provided. A datastructure is accessed that represents a communication decision treeconfigured to dynamically define individual trajectories through thecommunication decision tree using a machine-learning technique toindicate a series of communication specifications. The communicationdecision tree includes a set of branching nodes. Each branching node ofthe set of branching nodes corresponds to an action point configured toidentify a direction for a given trajectory. At a first time, it isdetected that a trajectory through the communication decision tree hasreached a first branching node of the set of branching nodes. Thetrajectory is associated with a particular user. In response to thedetecting that the trajectory has reached the first branching node,first learned data generated by processing first user data using amachine-learning technique is retrieved. The first user data includesuser attributes for a set of other users. Further in response to thedetecting that the trajectory has reached the first branching node, oneor more particular user attributes associated with the particular userare retrieved, one or more first communication specifications areidentified based on the first learned data and the one or moreparticular user attributes, and first content is caused to betransmitted to a user device associated with the particular user inaccordance with the one or more first communication specifications. At asecond time that is after the first time, it is detected that thetrajectory through the communication decision tree has reached a secondbranching node of the set of branching nodes. In response to thedetecting that the trajectory has reached the second branching node,second learned data generated by processing second user data using themachine-learning technique is retrieved. The second user data includesat least some user attributes not included in the first user data.Further in response to the detecting that the trajectory has reached thesecond branching node, one or more second communication specificationsare identified based on the second learned data and at least some of theone or more particular user attributes, and second content is caused tobe transmitted to the user device in accordance with the one or moresecond communication specifications.

In some embodiments, a computer-program product is provided that istangibly embodied in a non-transitory machine-readable storage medium.The computer-program product can include instructions configured tocause one or more data processors to perform operations of part or allof one or more methods disclosed herein.

In some embodiments, a system is provided that includes one or more dataprocessors and a non-transitory computer readable storage mediumcontaining instructions which, when executed on the one or more dataprocessors, cause the one or more data processors to perform operationsof part or all of one or more methods disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative embodiments of the present invention are described indetail below with reference to the following drawing figures:

FIG. 1 shows a block diagram of an interaction system.

FIG. 2 shows a template to be used for generating an emailcommunication.

FIG. 3 shows a template to be used for generating an app notificationcommunication.

FIG. 4 shows a representation of a communication decision tree.

FIG. 5 illustrates an example of a trajectory corresponding to a userdevice and extending through a communication decision tree.

FIG. 6 shows an exemplary interface to define a communication decisiontree.

FIG. 7 shows an exemplary parameter-defining interface for a switchicon.

FIG. 8 shows another exemplary parameter-defining interface thatincludes options to effect a bias towards or against representingvarious content in communications.

FIG. 9 shows another exemplary parameter-defining interface thatincludes options to effect a bias towards or against using variouscommunication channels to transmit communications.

FIG. 10 shows a flowchart for a process for using machine learning todirect trajectories through a communication decision tree according tosome embodiments of the invention.

FIG. 11 shows a flowchart for a process for defining amachine-learning-based communication decision tree using an interfacesupporting positionable visual elements.

DESCRIPTION

In some embodiments, systems and methods are provided that repeatedlyuse machine-learning data to facilitate iteratively identifyingcommunication specifications. More specifically, a communicationdecision tree is generated that includes a set of nodes. Each node cancorrespond to (for example) a detected event or a branching node thatcorresponds to a communication-specification decision and that isconnected to multiple next nodes representing a communicationcorresponding to one or more particular communications specifications.Each individual trajectory through the communication decision tree cancorrespond to an individual user and/or one or more particular userdevices. Each individual trajectory can extend across a particularsubset of the set of nodes, where nodes in the subset are representativeof specific actions initiated by the user and/or initiated at aparticular user device of the one or more particular devices, specificcharacteristics of a communication transmitted to the a particular userdevice of the one or more particular devices; and/or a decision to bemade as to a specification of an upcoming communication. For example, aspecification of an upcoming communication can indicate when it is to betransmitted, to which device it is to be transmitted, over which type ofcommunication channel it is to be transmitted, and/or what type ofcontent it is to include. In some instances, natural language processingcan be used to assign one or more categories to each of one or morecontent objects transmitted in a training set and/or to each of one ormore content objects available for transmission. A communicationspecification may then identify a particular category of content to betransmitted.

Each communication-specification decision can be made based on currentdata corresponding to the user and/or particular user devices, amachine-learning model and one or more learned parameters for themachine-learning model. The parameter(s) can be learned based on userdata associated with a set of other users and that indicates, for eachof the set of other users, one or more attributes of the other userand/or a set of events (e.g., user-initiated actions or characteristicsof communications transmitted to the user). The parameter(s) can furtherbe learned based on a trajectory target (e.g., identified by a client)that corresponds to a particular node within the communication decisiontree and/or a particular action initiated by the user.

A communication decision tree can be configured to include multiplebranching nodes, such that multiple communication-specificationsdecisions may be made for a single trajectory. In some instances, eachdecision is made using a same type of machine-learning algorithm (e.g.,a supervised regression algorithm). However, the algorithm may bedifferentially configured at each of the branching nodes, such that thebranching nodes differ with respect to (for example) the types of inputprocessed for each trajectory and/or the learned parameters to be usedto process input corresponding to a trajectory. In various instances,the algorithms for different branching nodes may be trained to optimizea same or different variable (e.g., based on an identification of a sametarget node or different target nodes). Not only may the branching nodesvary with regard to the types of input that the algorithm is configuredto process, but additionally the types of profile data available topotentially be processed for a given user can vary (e.g., profile datamay accumulate over time due to interaction monitoring). Further, thelearned data associated with any given node can change in time (due tocontinuous and/or repeated learning).

As one example, a trajectory for a user can be initialized upondetecting that profile data corresponding to the user includesinformation for at least a predefined set of fields. The profile datacan be collected using one or more web servers over one or more sessionsassociated with the user and/or retrieved from a remote data source. Insome instances, a user device automatically detects at least some of theprofile data and communicates it to the web server(s) (e.g., viaautomatically populated header information in a communication thatidentifies, for example, a unique device identifier, MAC address,browser type, browser version, operating system type, operating systemversion, device type, language to which the device is set, etc.). Insome instances, a communication includes data that represents user input(e.g., text entered into a web form, link selections, page navigation,etc.), which can then be logged as profile data.

Initializing the trajectory can include identifying a first node withinthe communication decision tree, which can include a first branchingnode. The first decision node can correspond to a decision as toidentify which of multiple content objects (e.g., identifying variousgroups of items and/or information associated with a web site) totransmit within an email communication to a device of the user. Thefirst decision node can also correspond to a decision as to when—withina two-day period—to send the email. The decisions can be made based onfirst learned data that indicates—for particular types of users—whattypes of objects and/or communication times are most likely to lead to atarget outcome. For example, the target outcome can include anoccurrence where the user activating a link within the email to access apage on the web site and/or the user interacting with the web site in amanner that corresponds to a conversion (e.g., a purchase of an itemrepresented on the web site), and the first learned data can indicatethat predictive factors as to which of three content objects will bemore effective at resulting in the target outcome include whether a usermost frequently uses a mobile device (versus a laptop or computer), theuser's age, and previous email-interaction indications as to for whichtypes of content objects the user clicked on a link.

Once the email is sent, the trajectory can extend to a node representingthe transmitted content until a next event is detected. The next eventcan include (for example) activation of a link within the web site,thereby indicating that the user is engaged in a current session withthe web site. Upon detecting this event, the trajectory can extend to asecond decision node to determine how to configure a requested web pageon the web site (e.g., in terms of whether to include dynamic contentobjects and/or how to arrange various content objects). In this example,second learned data indicates—for particular types of users—whatconfigurations are most likely to lead to a same target outcome. Forexample, the second learned data can indicate that predictive factors asto which of four configurations will be more effective at resulting inthe target outcome include whether a user most frequently uses a mobiledevice (versus a laptop or computer), a browser type, a current locationof a user device, and a current time of day at the user's location. Oncethe webpage (configured in accordance with the decision made at thesecond decision node) is sent, the trajectory can extend to a noderepresenting the configuration of the transmitted webpage. Thetrajectory can continue to extend upon detecting various user-initiated,system-initiated or external events (e.g., a passing of a predefinedtime interval since a previous event).

In this example, the target outcome remains the same across multipledecisions. However, rather than identifying a static workflow of actionsto perform—or even rather than determining a user-specific completesequence of actions to perform—techniques disclosed herein basedecisions pertaining to individual actions on current profile data,current learned data and current event detections. Machine learning isexecuted iteratively throughout a life cycle of a particular trajectoryto identify piecemeal actions to be performed. This approach canfacilitate high utilization of data (e.g., as expansions and/orevolutions of learned data and/or profile data can be utilized inmid-process decisions), which can promote achieving the targetobjective. Further, the approach enables a change (e.g., initiated by aclient) to a definition and/or constraint of a machine-learningtechnique to take a quick effect (e.g., as the change can stillinfluence trajectories having already been initiated). For example, aclient may change a target objective from conversion to retaining a userdevice on a web site for at least a threshold session duration. Modifiedparameters for machine-learning models associated with various branchingnodes can be determined and immediately effected, so as to affectpreviously initiated trajectories that subsequent reach the node(s).

Communication decisions (and/or partial directing through acommunication decision tree) can be based on anonymized or partiallyanonymized data, either or both of which can be built from anonymized,partially anonymized or non-anonymized data provided by one or moreproviders or clients. For example, a remote user-data management systemcan receive partially anonymized or non-anonymized data from one or moredata providers and can obscure or eliminate fields in individual recordsaccording to data-privacy rules and/or can aggregate field values acrossa user sub-population to comply with data-privacy rules. As describedherein, anonymized or partially anonymized data is data that has beenstripped of PII and/or aggregated such that individual data valuescannot be, beyond a certain probability, associated with particularpeople or users. Thus, the anonymized or partially anonymized data canlack or obscure sufficient data values to prevent identifying aparticular person as being a particular user or to prevent identifying aparticular person as having at least a threshold probability as being auser. For example, the anonymized or partially anonymized data may lacka name, email address, IP address, physical address and/or phone numberfrom profile data. The anonymized or partially anonymized data mayinclude or exclude certain demographic data, such as an age, city,occupation, etc. In some instances, anonymized or partially anonymizeddata is useful to collect so as to comply with privacy rules,regulations and laws, while also being able to process some of the data.The anonymized or partially anonymized data can include informationgathered from devices based on IP address range, zip code, date,categories of prior online inquiry, race, gender, age, purchase history,and/or browsing history, etc., which may have been gathered according toa variety of privacy policies and laws that restrict the flow ofpersonally identifiable information (PII), etc.

In some instances, the anonymized or partially anonymized data is usedto generate and/or update learned data (e.g., one or more parameters)associated with individual machine-learning configurations. This type oftraining need not (for example) require or benefit from data fields suchas contact information, so data records can be stripped of these fields.As another example, one or more sub-populations can be generated basedon values for a particular field, and specific values for that field maythereafter be replaced with an identifier of a sub-population.

In some instances, profile data corresponding to a particular user forwhich decisions are being made include the anonymized or partiallyanonymized data. For example, a system can detect that a trajectory hasreached a branching node and request data from a user-data managementsystem (e.g., using a device identifier or other identifier associatedwith the trajectory). The system can return profile data that includes(for example) one or more specific non-anonymized field values, one ormore field values that have been generalized (e.g., assigned to acategory), and/or eliminated field values. The non-anonymized field datamay be included in profile data when (for example) such field valueswere supplied by (e.g., upon having been collected using data-collectingfeatures built into a webpage and/or via a transmission from the client)or otherwise accessible to (e.g., via a data-sharing agreement) a clientfor which a decision is being made. The system may also returnpopulation data (e.g., that can itself be learned and/or can evolve overtime) that indicates relationships between field values, which may beused to estimate values or categories for missing field values.

FIG. 1 shows a block diagram of an interaction system 100. A machinelearning data platform 105 can include one or more cloud servers and canbe configured to receive user data from one or more client systems 105.The user data can include anonymized or partially anonymized user data(stored in an anonymized user data store 115) and/or secureclient-availed user data (stored in a secure client-availed user datastore 120), which may be less anonymized than anonymized user data ornot anonymized. When secure client-availed user data is received, it maybe securely stored in association with an identifier of a client, suchthat other clients cannot gain access to the data. The data may bestored in a multi-tenant cloud storage system such that multiple clientscan log in to a central location to access a server or collection ofservers, but where the specific access to data is controlled dependingon which client has authenticated to the cloud storage system.Anonymized or partially anonymized user data may, or may not, beparticularly configured for various clients (e.g., depending on whichdata the client supplied and/or data-sharing agreements associated withthe client). Thus, a profile data populator 122 at machine learning dataplatform 105 can generate profile data corresponding to one or moreindividual users for particular clients and can customize which fieldvalues are included in the profile data for individual clients.

In some instances, profile data populator 122 enhances a profile dataset to supplement client-availed user data with partially anonymizeduser data, which can (as aggregated) define client-specific learned data(stored in a client-specific learned data store 130) for a given user.For example, data from a profile in the client-availed data can bemapped to one or more data sets in the anonymized or partiallyanonymized user data, such that richer data sets can be used in themachine-learning analyses. The mapping may occur using overlapping data(e.g., an IP address, if included in the anonymized or partiallyanonymized user data, a purchase time, a pseudo-random user identifierassigned by a client, etc.).

A machine learning model confugerer 123 can configure a given machinelearning model based on (for example) an identified target outcome,available training data, one or more client-identified constraintsand/or potential actions as indicated by a communication decision treeand/or by a client. Configuring the machine learning model can includedefining one or more parameters for a particular instance of the model(e.g., the instance being associated with a particular branching node,client and/or time period).

Each parameter can be indicative of a relationships and/or correlationbetween user attributes (stored in a learned parameter data store 125).The parameter(s) can include a weight that indicates how and/or anextent to which a first user attribute is predictive of a second userattribute that corresponds to an indication as to whether and/or anextent to which a target outcome occurred. The weight may be definedalong a discrete or continuous value range and/or can be binary.

As one example, the parameter(s) can indicate which attributes fromamongst a set of attributes are predictive of future occurrence of aparticular type of conversion event. For example, it may be determinedthat having visited a webpage associated with a “travel” tag more thantwice in the last month was a predictor of buying a piece of luggage. Asanother example, it may be determined that having visited a movie-reviewwebpage within a given day was a predictor for later purchasing anonline rental of a movie. Indirect associations and trends may also belearned, such as identifying there is an inverse correlation between anage of the user and an average time spent online each day. Eachparameter may be associated with a strength and/or confidence of arelationship, optionally with serial associations between the datapoints gathered and the conclusions being made, where each associationin serial carries a certain probability that the data at the start ofthe association is accurate for what it says and a certain otherprobability that the association itself is accurate.

The configuring may, but need not, be performed using client-availedprofile data and/or to produce client-specific parameters. Theclient-specific parameter(s) may be, for example, a modified version ofthe parameter(s) generated using the anonymized or partially anonymizedprofile data.

Various machine-learning techniques may be used to generate learneddata. For example, a machine-learning technique may use decision-treelearning, association-rule learning, an artificial neural network, deeplearning, inductive logic programming, a support vector machine,clustering, a Bayesian network, reinforcement learning, representationlearning, similarity and metric learning, sparse dictionary learning, agenetic algorithm, or rule-based machine learning. In some instances, amachine-learning technique includes an ensemble technique, which learnsinter-ensemble weights to apply to results produced from variousunderlying techniques (such as two or more of those previouslymentioned). The inter-ensemble weights may be identified based on (forexample) accuracy, speed and/or resource usage associated with theunderlying techniques.

Training a machine-learning technique (to identify one or moreparameters) can include identifying how a set of observed inputs (e.g.,content of a marketing email, content of a promotion, and/or theconfiguration of a web site) relates to a set of corresponding outputs(e.g., an outcome, such as the presence or absence of certain conversionevent, for a corresponding marketing email, a corresponding promotion,and/or a corresponding web site configuration). These observedobservations can be used to identify modeled relationships and/ortrends, with a goal of predicting candidate factual information (e.g., apredicted next input to be received or a predicted output based oncertain inputs) that has not yet occurred based on factual informationleading up to the candidate factual information. Each prediction cancarry a confidence or probability, and chains of predictions have acombined confidence or probability.

Thus, machine learning model configurator 123 can identify modelparameters for particular client systems 110 based on (for example)target outcomes, client-specific profile data and/or machine-learningtechniques. Client-specific learned data can be selectively shared witha client system having provided the underlying client-availed profiledata. Client system 110 can include a system that hosts one or more websites, hosts one or more apps and/or causes emails to be transmitted.For example, client system 110 can include a web server 135 thatreceives and responds to HTTP requests for pages on one or more domainsand an email server 140 that delivers emails to users' email addresses.Client system 110 may further or alternatively include an app server 145to receive and respond to requests received via an application executingon a user device. Thus, one or more servers at client system 110 can beconfigured to detect requests from one or more user devices 150-1, 150-2and/or trigger transmission of content to one or more user devices150-1, 150-2. User devices 150-1, 150-2 may include, for example, acomputer, smart phone, tablet, etc. It will be appreciated that, invarious circumstances, a single user device may be associated with asingle user or more than one users. Further, a single user may beassociated with a single user device or more than one user devices.

Web server 135 and/or app server 145 may store indications of requestsfor content (e.g., a webpage or app page) from a content library 153 asuser data in a client-managed user data store 150. The stored data mayinclude automatically detected information (e.g., a request time) alongwith information included in the request (e.g., a device identifier, IPaddress, requested webpage, user-entered input, etc.). Storing the datamay include updating a profile to include the data. Web server 135,email server 140 and/or app server 145 may further store data inclient-managed user data store 150 that indicates which content wasdistributed to particular user devices (e.g., by identifying atransmission time, user-device identifier, content-object identifier(s),and/or type of communication).

Client system 110 can transmit at least part of the user data fromclient-managed user data store 150 to machine learning data platform105, which can store it in secure client-availed user data store 120.The transmission(s) may occur periodically, during a request forclient-specific learned data, at regular time intervals, etc. In someinstances, client system 110 at least partly anonymizes some or all ofthe user data (e.g., by omitting or obscuring values for at least somefields) before transmitting it to machine learning data platform (e.g.,such that it is stored as anonymized or partially anonymized user dataat the platform). In some instances, the data is not at least partlyanonymized, such that the data is either stored in secure client-availeduser data store 120 or is at least partially anonymized at machinelearning data platform 105. For some datasets, the anonymized orpartially anonymized data is received from a third party, after beingstripped of PII, and stored by client system 110 without ever havingaccess to the non-anonymized data. In some embodiments, the anonymizedor partially anonymized data is natively anonymized or partiallyanonymized. In these embodiments, websites may run embed scripts ontheir web sites that, when executed, gather anonymized or partiallyanonymized data about accesses of the web sites by users. The scriptsmay gather only information that may be gleaned without knowing a user'spersonal information and stored in a data cloud that ensures that useridentity can never be deduced beyond a certain probability.

Client system 110 can store machine-learning data in a machine learningdata store 155. In some instances, the machine learning data includes anindication of one or more decisions made at a branching node for a giventrajectory, one or more content specifications identified using acommunication decision tree and/or one or more parameters. Themachine-learning data can be requested from, received from and/orderived from data from machine learning platform 105. For example, insome instances, machine learning model configurator 123 causesparameters generated for and/or applicable to a client to be transmittedto client system 110. As another example, a machine learning modelimplementor 157 can apply machine learning model configured withparticular parameters to particular profile data to identify one or moreparticular communications specifications to define a communicationsaction to be taken for a client (and/or a next node of a trajectory)that corresponds to the profile data. Machine learning model implementor157 can then cause an indication of the identified communications actionand/or the next node to be transmitted in association with an identifierof a trajectory, user and/or user device.

Identifying a next node and/or communications specification(s) caninclude running a machine learning model (associated with a currentbranching node) using particular profile data and one or more learnedparameters. A result can indicate (for example) which of variouscontent-presentation characteristics is associated with a high (e.g.,above-threshold) or highest probability of leading to a particulartarget outcome (e.g., target conversion). In some instances, theanalysis includes identifying one or more content-presentationcharacteristics associated with a highest probability of leading to aparticular conversion target outcome. In some instances, the analysisbalances the probabilities of leading to a particular conversion resultswith a predefined cost metric associated with variouscontent-presentation characteristics.

In some instances, running the machine learning model using theparameters (e.g., at machine learning data platform 105 or client system110) can include (for example) performing a regression analysis usingthe profile data and parameters to generate a number that can becompared to one or more thresholds. The one or more thresholds candefine two or more ranges (e.g., open-ended or closed ranges), with eachrange corresponding to a particular next node and/or communicationsaction. In some instances, running the machine learning model using theparameters can include processing at least part of the profile data andat least part of the parameters to produce a result that can be comparedto (e.g., via calculation of a difference, calculation of a cost using acost function, etc.) each of a set of reference data variables (e.g.,single values, a vector, a matrix, a time series, etc.)—each beingassociated with a particular next node and/or communications action andeach potentially defined at least in part based on a parameter. A nodeor communication associated with a reference data variable for which thecomparison indicated a closest correspondence can be selected.

A dynamic content generator 147 can trigger a presentation of a contentobject in accordance with the selected communication specification(s).To generate an appropriate instruction, dynamic content generator 147may first identify what communication channel is to be used to transmitthe object, the type of object that is to be transmitted, a version ofcontent that is to be transmitted and/or when the content object is tobe transmitted. The identification can be determined based on (forexample) a result of an implementation of a machine learning model, aconfiguration of a machine learning model (e.g., which may restrainpotential options with respect to one or more of these options), and/orone or more parameters.

Dynamic content generator 147 can identify a type of communication(e.g., email, SMS message, pop-up browser window or pushed app alert) tobe transmitted, which can inform (for example) which of web server 135,email server 140 and/or app server 145 is to transmit the communication.The identification can be made explicitly (e.g., based on amachine-learning result, parameter, and/or machine-learning-modelconfiguration) or implicitly (e.g., due to a selected content objectbeing of a particular type).

Identifying the content object can include selecting from amongst a setof existing content objects or generating a new content object. Thecontent object can include (for example) a webpage, an object within awebpage, an image, a text message, an email, an object within an emailand/or text. In some instances, a result of executing a configuredmachine-learning model on profile data identifies a particular contentobject. In some instances, a result identifies a characteristic ofcontent (e.g., having a particular metadata category) and/or identifiesa particular technique for selecting content. For example, a result mayindicate that a “tools” item is to be featured in a content objectand/or that a communication is to include four content objects thatcorrespond to four different (though unspecified) categories. In suchinstances, dynamic content generator 147 can (for example) select fromamongst a set of potential content objects using a selection techniquethat is (for example) indicated via a result of the machine-learningimplement, via a parameter, and/or via a predefined setting. Forexample, a selection technique may indicate that a selection techniqueis to include a pseudo-random selection technique, a technique toidentify a most recently added object, a technique to identify ahighest-conversion object within a set of potential content objects(e.g., having one or more attributes as indicated in a machine-learningresult).

In some instances, a time at which a communication is to be transmittedis explicitly identified (e.g., based on a machine-learning result,parameter, and/or machine-learning-model configuration). For example, atime range can be defined as beginning with a current time and endingwith a client-identified maximum time. The model may evaluate a set ofregularly spaced potential transmission times within the time range. (Insome instances, each potential transmission time is considered multipletimes in combination with other potential specifications, such ascontent categories or communication channels.) A machine-learning modelresult can identify a transmission time associated with a highestprobability of resulting in a target outcome. (Notably, if combinationsof specifications are considered, the transmission time may include thetime in a combination associated with the highest probability. In someinstances, a communication is transmitted immediately, upon receiving anext request for content (e.g., corresponding to a given web site orapp) from a user device associated with a machine-learning result, or inaccordance with a predefined transmission schedule.

In some instances, each specification corresponding to a communicationis identified (e.g., during a task and/or using a machine-learningmodel, a machine-learning configuration, a parameter, a client rule,etc.) at or before the communication is transmitted. Thus, all or someclient-controlled configuration of the communication and/or itstransmission can be performed prior to transmission of thecommunication. In some instances, at least one specificationcorresponding to a communication is identified (e.g., during a taskand/or using a machine-learning model, a machine-learning configuration,a parameter, a client rule, etc.) after the communication istransmitted. Thus, at least some client-controlled configuration of thecommunication and/or its transmission can be performed aftertransmission of the communication. This post-transmission configurationcan thus be based upon learned data and/or profile data that was notavailable prior to the transmission of the communication. For example,additional profile data corresponding to a user may become availablebetween a first time at which an email was transmitted and a second timeat which the email is opened and rendered. The transmitted email caninclude a script that executes when the email is to be rendered. Thescript can cause a request to be issued to identify device properties,such as a layout and/or application type. The script can pass theseproperties along with a request for content to be presented to a server.Thus, the server can select content and/or identify one or more displayconfigurations based on specific rendering information, current profiledata and/or current parameters to direct a selection of specificcontent.

As an additional or alternative example, the communication may containone or more references or links to pages that, when opened (e.g., in aweb browser), render content for display. The pages targeted by thelinks may include some content that was determined, by the machinelearning engine, before or at the time the communication was generated.The pages can further be configured to include content that is to beselected or generated when a request for rendering the pages is detected(e.g., when a script detects activation of a link) and/or when the pagesare being generated or rendered (e.g., as indicated by executing ascript as part of loading the page). In some instances, a script in theemail identifies the content configuration at the time of rendering orat the time that rendering is requested. In some instances, a scriptexecuting on the linked page identifies the content configuration.

As one example, a client system may offer online purchases of fooddelivery. It may be detected that a particular user had looked at a menufor a given restaurant at 2 pm. The client system may retrieve a set ofuser attributes from a profile data for the user from its client-manageduser data. Client-specific learned data may indicate that there is a 76%chance that the user will make a purchase from the restaurant if anemail including a discount code is sent in the evening to the user(e.g., as compared to a lower probability associated with other types ofcommunication and other times). In response to determining that the 76%chance is above a 65% threshold for sending a discount threshold, emailserver 140 transmits an email to the user device. The email includes ascript that, when executed, identifies the restaurant and discount to bepresented. The user opens the email the next day at 10 am. The code isexecuted to request the restaurant and discount from the client system.The client system has since received updated public learned correlationdata. The client system inputs the time, the user's location (as she isnow at work) and prior purchase information to a decision tree builtbased on the learned data. It is determined that the discount is to be10% (e.g., to maintain a threshold likelihood of conversion) and therestaurant is to be a deli near the user's work (e.g., to maximize alikelihood of conversion), whereas—had the user opened the email thenight before, different user attributes and learned data would haveresulted in a 15% discount (e.g., to maintain the threshold likelihood)from an Indian restaurant near the user's home (e.g., to maximize thelikelihood). The email includes a link to order from the deli. When theuser clicks on the link, the web server determines what content is to bepresented—specifically, which food items are to be recommended. Therecommendations are based on even more recently updated public learnedcorrelation data, which indicate that salads and sandwiches should berecommended over soup and entrees, as the former options have beenrecently popular (predicted to be popular due to the warmer weather).Thus, this example illustrates how content presentations can bedynamically customized for a given user based on very recent learneddata and user attributes.

Machine learning data platform 105 can generate updated client databased on (for example) any communications received from a user device(e.g., responsive to a workflow action). For example, the updated clientdata can include one or more new fields generated based on data in aheader or payload of a received communication, an indication as towhether (e.g., and when) a particular event was detected, and/or acurrent or final stage of the workflow to which the profile is assigned.Machine learning data platform 105 can avail the updated client data(e.g., along with corresponding profile identifiers) to client system110, which can store the updated data in client-specific learned datastore 165. Client system 110 may, but need not, separately store theupdated data from underlying profile(s).

It will be appreciated that, in some instances, some or all of machinelearning data platform can be incorporated within client system 110. Insome instances, client system 110 communicates with machine learningdata platform during iterations of a communication decision tree. Forexample, client system 110 (e.g., web server 135 or app server 145 atclient system 110) may detect a flag (e.g., included in a URL) in arequest for web content or app content received from a user device,where the flag indicates its association with a machine-learning-basedworkflow). Client system 110 may then alert machine learning modelimplementor 157 of the request, so that a trajectory can beappropriately updated.

Machine learning data platform, client system 110 and user devices150-1, 150-2 can communicate over a network 160, which can include, forexample, the Internet, a local area network, a wide area network, and soon. It will be appreciated that various alternatives to the depicted anddescribed embodiments are contemplated. For example, some or all of themachine learning may be performed at client system 110. Client system110 may periodically receive anonymized or partially anonymized userdata to process using a machine-learning technique.

Other techniques for using and configuring communication decision treesare detailed in U.S. application Ser. No. ______, filed on Jun. 13, 2018(entitled “Methods and Systems for Configuring Communication DecisionTrees based on Connected Positionable Elements on Canvas”), and U.S.application Ser. No. ______, filed on Jun. 13, 2018 (entitled“Machine-Learning Based Processing of De-Obfuscated Data for DataEnrichment”). Each of these applications is hereby incorporated byreference in its entirety for all purposes.

FIGS. 2 and 3 illustrate interfaces 200 and 300 for configuringtemplates 202 and 302 for communications configured to be partlyconfigured upon detecting a rendering process or at rendering. Theconfiguring can include executing a configured machine-learning modelusing current learned configurations of the model and current profiledata. Template 202 shown in FIG. 2 includes a template to be used forgenerating an email communication, and template 302 shown in FIG. 3includes a template to be used for generating an app notificationcommunication.

Template 202 includes static text (e.g., text 205) and interactionfeatures (e.g., button 210). Template 202 further represents aparticular layout, in which three items are to be linearly representedabove text 205. Template 202 also include dynamic components (e.g.,dynamic text 215 and dynamic image 220) that are configured to beidentified when rendering of the email is requested or occurring. Thus,when an email communication is transmitted, the static components can betransmitted along with code configured to (upon detecting a request torender the email) locally identify at least part of current profiledata, request at least part of current profile data, requestidentification of dynamic components, receive or retrieve dynamiccomponents (e.g., identified using current profile data, currentanonymized or partially anonymized data and/or current learnedparameters) and/or generate a complete email based on the template anddynamic components. The generated email can then be presented.

Template 302 includes a static layout and multiple dynamic textcomponents (e.g., a dynamic title section 310. Template 302 can beconfigured to be transmitted with a script that facilitates dynamicallyidentifying each dynamic text component. For example, the scriptcan—upon detecting a request to present the notification (e.g., inresponse to opening an app, clicking on a notification app element,etc.)—locally identify at least part of current profile data, request atleast part of current profile data, request identification of dynamictext components, receive or retrieve dynamic text components (e.g.,identified using current profile data, current anonymized or partiallyanonymized data and/or current learned parameters) and/or generate acomplete notification based on the template and dynamic text components.The generated notification can then be presented. Interface 300 shows anexample of a dynamically generated notification 315 this includes thestatic layout and particular dynamic text.

FIG. 4 shows a representation of a communication decision tree 400.Communication decision tree 400 includes a starting node 405, at whicheach trajectory begins. A particular trajectory can be (in this example)initialized upon detecting that a user has completed two particularactions (e.g., initialized two web-site sessions, purchased two itemsfrom a web site, navigated to at least two webpages on a web site,etc.).

Communication decision tree 400 includes three branching nodes 410, 415and 420—each of which branches to connect to three nodes representingthree different actions. A trajectory can automatically and immediatelyextend from initial node 405 to a first branching node 410, whichtriggers a first decision to be made. Specifically, the first decisioncan include identifying a communication channel to use to send an alertof a web-site feature. The alert can include an automatically presentedstatic header that indicates (for example) that a product or discount(generally) is available in association with the web site. The alert mayfurther be associated with dynamic content (e.g., that specificallyidentifies one or more products and/or a discount) that is to beidentified at a second branching node 415 upon detecting a request toopen the notification.

First branching node 410 is connected to a first action node 425 a thatrepresents an email communication channel, a second action node 425 bthat represents an SMS-message communication channel, and a third actionnode 425 c that represents an app-based communication channel (where anotification would be pushed to and/or by an app installed at a userdevice).

The first decision can be made using a machine-learning model configuredbased upon one or more first parameters. The one or more firstparameters can be dynamically determined based on anonymized and/orpartially anonymized user data and/or client-specific data. For example,anonymized and/or partially anonymized user data may indicate—for eachof various user sub-populations (as defined based on one or more userattributes)—how effective an alert transmission sent via each of thethree types of communications channels was at triggering the user toinitiate a session at a corresponding web site (e.g., as determinedbased on using tracking links within the alerts) and complete atransaction during the session. The anonymized and/or partiallyanonymized user data may correspond to many different web sites and/orweb sites having one or more particular characteristics. Theclient-specific data can include data tracked by a given client for theparticular web site of interest and can data that specificallyidentifies each user to which various alerts were transmitted and theresult. The client-specific data may thus be richer in some respectsrelative to the anonymized and/or partially anonymized data, but thenumber of users represented in the client-specific data may be smallerthan that represented in the anonymized and/or partially anonymizeddata. Further, the client-specific data may lack pertinent attributecombinations. For example, a given client may not have previously usedapp-based alerts, which may have reduced an accuracy with which amachine-learning model could predict potential effects of such alerts.

The machine-learning model (configured with the first parameters) canuse profile data associated with the trajectory to determine whichcommunication channel to user. The profile data can includeclient-collected profile data (e.g., using metadata, cookies and/orinputs associated with previous HTML requests from a user deviceassociated with the trajectory). The profile data may further includeother profile data requested and received from a remote user-profiledata store, which may collect and manage profile data from multiple webhosts, clients, etc.

Upon identifying the communication channel, the trajectory extends tothe corresponding action node (425 a, 425 b or 425 c). An alert is thensent using the corresponding communication channel. The alert can beconfigured to automatically identify limited content and to cause thetrajectory to extend to second branching node 410 upon detecting arequest to open the alert. A decision can then be made at secondbranching node 410 to determine specific content to be presented in abody of the alert.

Thus, second branching node 415 is connected to a first notificationcontent node 430 a that represents content that identifies a productmost recently viewed by the user at the web site, a second notificationcontent node 430 b that represents content that identifies four of theproducts most viewed (across users) at the web site over the last week,and a third notification content node 430 c that represents content thatincludes an identification of a discounts. The second decision can bemade using the machine-learning model configured based upon one or moresecond parameters. Thus, in some (but not all) instances, a general typeof machine-learning model used at various branching nodes to makedecisions can be the same, though particular configurations (e.g.,indicating weights to be assigned to various user attributes, which userattributes are to be considered at all and/or target outcomes) candiffer.

The one or more second parameters can be dynamically determined based onanonymized and/or partially anonymized user data and/or client-specificdata. However, each of the anonymized and/or partially anonymized userdata and/or the client-specific data may have changed since making thefirst decision, which can contribute to differences between the firstand second parameters. Further, the potential actions considered atsecond branching node 415 differs from those considered at firstbranching node 410. Therefore, the first and second configurations canbe different. Additionally, the profile data that is processed candiffer between the first and second branching nodes. For example, aclient-associated application may have been installed at a user devicebetween processing performed at the first and second branching nodes(e.g., such that application-based notifications are an option at thesecond branching node but were not at the first).

Upon identifying the content, the trajectory extends to thecorresponding content node (430 a, 430 b or 430 c). The correspondingcontent is then transmitted to the user device, such that it can bepresented at the user device.

The content can include one or more tracking links to a webpage at theweb site. Upon detecting that a tracking link has been activated, thetrajectory can extend to a third branching node 420. A decision can thenbe made at third branching node 415 to determine specific content to bepresented at the requested webpage.

Thus, third branching node 420 is connected to a first webpage contentnode 435 a that represents content that identifies four representativeproducts—each associated with a different category; a second webpagecontent node 435 b that represents content that identifies fourrepresentative products—each associated with a same category; and athird webpage content node 435 c that represents content that identifiesa single product predicted to be of interest to a given user based onprevious webpage-interaction data.

The third decision can be made using the machine-learning modelconfigured based upon one or more third parameters. The thirdparameter(s) can differ from the first parameter(s) and/or the secondparameter(s) as a result of temporal changes to anonymized and/orpartially anonymized user data, the client-specific data and/or as aresult of differences of the potential actions. Additionally, theprofile data processed at third branching node 420 can be different thanthat processed at first branching node 410 and/or second branching node415 (e.g., as a result of detecting new metadata in communications fromthe user device and/or receiving new information corresponding to theprofile from a remote system).

Upon identifying the content, the trajectory extends to thecorresponding content node (435 a, 435 b or 435 c). The correspondingcontent is then transmitted to the user device, such that it can bepresented at the user device within a corresponding webpage.

It will be appreciated that, while communication decision tree 400depicted in FIG. 4 shows a single decision being made at eachcommunication stage (when a notification is to be transmitted, when abody of a notification is to be presented, and when a webpage is to bepresented), multiple decisions may instead be made using amachine-learning model. For example, at branching node 410, a decisionmay be made as to what communication channel to use and when to transmita notification (e.g., by identifying a time within a time period or atime from amongst a set of potential times). As another example, aseparate decision may be made before or after the communications-channeldecision to identify a transmission time. Thus, a machine-learning modelmay be configured to generate multiple outputs or multiplemachine-learning models can have multiple configurations (eachcorresponding to different parameters and/or hyperparameters, eachtrained separately and/or each producing a separate type of output).

FIG. 5 illustrates an example of a trajectory 500 corresponding to auser device and extending through communication decision tree 400. Inthis instance, a machine-learning result made at first branching node410 indicated that an email communication channel was to be used to senda notification, such that trajectory 500 extended to first action node425 a. An email notification is then transmitted to the user device. Arequest for email content is detected, indicating that a user isattempting to view the email, such that trajectory 500 extends to secondbranching node 415. There, a decision is made to include content thatincludes an identification of a discounts in the email. Thus, trajectory500 extends to third notification content node 430 c, and thecorresponding content is transmitted to the user device.

A request for a webpage corresponding to a targeted link within theemail is then detected, such that trajectory 500 extends to thirdbranching node 420. A machine-learning result is generated thatindicates that the webpage is to include content that identifies fourrepresentative products—each associated with a different category.Therefore, trajectory 500 extends to first email content node 435 a, atwhich the corresponding webpage content is transmitted to the userdevice.

In the depicted instance, the decisions at the first branching node, thesecond branching node and the third branching node are indicated ashaving been made at 5 pm on a first day, 12 pm on a second day, and 6 pmon the second day. Corresponding actions are then immediately performed.It will be appreciated that action times may further be decided inaccordance with a machine-learning model execution, client rule or othertechnique.

It will be further appreciated that identifying themachine-learning-based decision can include implementing one or moreadditional constraints and/or factors. Alternatively or additionally,the machine-learning-based decision can be further modified based on oneor more additional constraints and/or factors. For example, U.S.application Ser. No. 14/798,293, filed on Jul. 13, 2015, (which ishereby incorporated by reference in its entirety for all purposes)further details additional techniques to dynamically identifycommunication characteristics, which may be further combined withmachine-learning techniques disclosed herein.

In some embodiments, systems and methods are provided that avail acanvas to facilitate configuring a sequence of machine-learningimplementations to partly define a communication exchange. Morespecifically, a canvas is availed that accepts positioning andconnecting of individual switch visual elements with corresponding setsof communication visual elements. A communication decision tree can thenbe generated based on a set of positioned and connected visual elements.The canvas can be configured to accept, for each communication visualelement, an identification of one or more communication specificationsassociated with the communication visual element. Each switch visualelement can represent a machine-learning technique (to be associatedwith particular parameters learned through training) to be used toselect a particular communication visual element from amongst a set ofcommunication visual elements connected to the switch visual element.The canvas can be configured to accept (e.g., for each switch visualelement or generally) an identification of a target outcome (e.g.,representing a user-initiated event or communication), which can directmachine-learning selections. Thus, the particular communication visualelement selected using the machine-learning technique can correspond toa communication specification predicted to be relatively likely toresult the target outcome (e.g., which may be represented as an eventvisual element in the communication decision tree).

A machine-learning model can be defined for each represented switchvisual element. The machine-learning model can be trained using previoustrajectories pertaining to other communication decision trees (e.g., butcapitalizing on the other communication decision trees havingcommunication visual elements that correspond to similar or samecommunication specifications as those represented by communicationvisual elements in the model being trained). For example, the model canbe trained by determining—for the trajectories routed so as to trigger acommunication having a particular communication specification—whatsubsequent user-initiated events were represented by those trajectories(e.g., and what portion of the trajectories represented an occurrence ofa client-identified target outcome). The model can further oralternatively be trained using trajectories as they emerge that pertainto the generated communication decision tree.

In some instances, a model can be trained using a data set that reflectsprevious events (e.g., trajectories through a same or differentcommunication decision tree and/or other indication of an eventsequence) and is augmented with new data. The new data may have recentlybecome available (e.g., via newly received form input or metadatadetection) but may correspond to a variable type estimated to be staticor predictably changing. For example, if a user's age is identified attime X, the user's age at time X−3 years can be calculated, while anaccuracy of a retrospective estimate of an interest or location variableover an extended time period may be less reliable. The training can thendetermine whether various attributes represented in the new data waspredictive of whether particular events were going to occur.

The interface can be configured to accept indications as to biases thatare to be applied at various machine-learning stages. For example, withrespect to a given switch element that is connected to a particular setof communication visual elements, a client may interact with a slidercontrol visually associated with a first visual element to indicate thatpath selections are to be boosted towards (or dampened from) the firstvisual element. Metadata that feeds into the machine-learning model canbe set based on the interaction to enable effecting of a correspondingbias. In some instances, the metadata can correspond to an unlearnedhyperparameter that is then used to adjust or constrain a learnedparameter (e.g., weight). In some instances, the metadata be used todefine a post-processing adjustment to be applied to a result generatedby the machine-learning model. In some instances, a client or systemimplements a bias towards a given communication visual element whentraining data corresponding to a communication specification representedby the element is relatively low (e.g., generally and/or in associationwith a given communication stage).

In some instances, an interface can enable a client to define astructure of the communication decision tree and/or—for each decisionnode—one or more hyperparameters of a machine-learning model to beexecuted at the node. It will be noted that a machine-learning model canbe defined based on one or more hyperparameters and one or moreparameters. Each of the one or more hyperparameters includes a variablethat is not learned via training of the machine-learning model, whilethe one or more parameters include one or more variables that arelearned via training of the machine-learning model. Thus, an interfacecan be configured to allow a client to specify hyperparameters thatindicate (for example) a number of branching nodes, actionscorresponding to each branch connected to each branching node, otherinter-node connections, one or more constraints to be observed duringexecution of individual machine-learning models, and so on.

FIG. 6 shows an exemplary interface 600 to define a communicationdecision tree. Specifically, interface includes a canvas 605 on whichrepresentations of various nodes can be positioned and connected.Interface 600 can include a set of icons that can be selected andpositioned on canvas 605 to represent specific sequential operations.The set of icons can include a start icon 610 representing a start ofthe communication decision tree. Start icon 610 can be associated withconfiguration logic that can receive a definition of a condition that,when satisfied, indicates that a trajectory through the communicationdecision tree is to be initiated.

The set of icons can further include an end icon 615. The communicationdecision tree can be defined to indicate that a given trajectory iscomplete upon reaching end icon 615. A client can then connectaction-defining icons and/or event-detection icons between a positionedstart icon 610 and a positioned end icon 615 to represent variousoperations and assessments that are to be performed during trajectoryobservations.

An action-defining icon included in the set of icons can be a switchicon 620. Switch icon 620 corresponds to a branching node, at which abranch is selected or “switched to”. The selection can be made using aconfigured machine-learning model and profile data. In many instances,switch icon 620 is connected to multiple potential paths. A potentialpath can intersect with another icon (e.g., a communication icon,event-detection icon, other switch icon and/or an end icon).

Exemplary communication icons include an email icon 625 indicating thatan email is to be transmitted to a user device, a text-message icon 630indicating that a text or SMS message is to be transmitted to a userdevice, and an app-message icon 635 indicating that an alert is to beindicated via an app installed at a user device. In some instances, apotential path indicates that no action is taken (via a lack of acommunication icon). In the depicted canvas, the positioned switch iconis connected to three paths: two email paths (e.g., associated withdifferent content and/or transmission times) and one no-action path.

An event-detection icon included in the set of icons can includetarget-detection icon 637, which represents that an event thatcorresponds to a target outcome for one or more machine-learningtechniques has been detected. Target-detection icon 637 and/or anotherevent-detection icon can indicate (for example) that a notification hasbeen opened, a targeted link included in a notification has beenactivated, a user device associated with a trajectory has initiated asession with a web site, a product (e.g., any product or a specificproduct) has been purchased on a web site, additional profileinformation corresponding to the trajectory has been provided, and soon.

Interface 600 can include a connection tool 640 that can be used toconnect multiple icons in a directional manner. Each connection canindicate that the communication decision tree is configured to allow atrajectory to extend across the connected node in the indicateddirection. However, each connection can be associated with a condition,such that a trajectory only extends across the connection when thecondition is satisfied. For example, a connection can be configured suchthat a condition is satisfied when a determination is made at abranching node (connected at one end of the connection) to perform anaction represented by a communication icon (connected at another end ofthe connection). As another example, a condition may be configured to besatisfied upon detecting a particular type of interaction in associationwith a trajectory-associated user device.

Each action-defining icon can be associated with one or more actionparameters that define a detailed action to be performed when atrajectory has reached the icon. For example, a parameter-defininginterface may presented as part of interface 600 upon clicking on anicon and/or a parameter-defining interface can be opened in a pop-upwindow upon right-clicking on and/or double-clicking the icon.

In some instances, each action-defining icon and/or event-detection iconcorresponds to a widget or piece of code that can be independentlyexecuted. Canvas 605 can serve as a communication fabric, such that aresult produced by one widget (e.g., an indication from amachine-learning model corresponding to a switch icon that acommunication is to be transmitted in accordance with a particularcommunication specification) can be availed to another widget (e.g., awidget corresponding to a communication icon corresponding to theparticular communication specification). Thus, canvas 605 can extendtrajectories in response to widget results and orchestrate aspects ofcommunication exchanges.

While not shown in FIG. 6, it will be appreciated that, in someinstances, multiple switch icons 620 can be positioned on canvas 605.Each switch icon 620 can correspond to a separate instance of amachine-learning model that can be separately configured and operated.

FIG. 7 shows an exemplary parameter-defining interface 700 for a switchicon. Parameter-defining interface 700 includes a field for a StageLabel that accepts text input. The text input can subsequently displayednext to the associated icon in the interface for defining thecommunication decision tree. A description can also be entered via textinput, which can be displayed (for example) in the interface fordefining the communication decision tree in response to detecting asingle click or double click in association with the icon.

For switch icons that are configured to identify a selection or actionspecification and/or that are configured to implement a machine-learningmodel, parameter-defining interface 700 can include a field to define atarget outcome. For example, a pull-down menu may identify a set ofevents that are being tracked and are available for identification as atarget outcome. The target outcome can include an action initiated at auser device, a system-initiated notification, etc. For example, a targetoutcome can include detecting that a link within a communication availedto the user device was clicked, that a communication availed to the userdevice was opened, that a communication resulted in a purchase made inassociation with the user device (i.e., a conversion), that a chatsession was initiated, that a form was completed, etc.

For switch icons that are configured to identify a selection or actionspecification and/or that are configured to implement a machine-learningmodel, parameter-defining interface 700 can further include one or morefields that indicate potential results to be identified. For example,interface 700 includes fields that correspond to three paths or branchesthat extend from the icon. In this instance, a stage-label name ofanother action-defining icon is identified for each path. In someinstances, path information is automatically updated atparameter-defining interface 700 upon detecting that a switch isconnected to one or more other icons at the interface for defining thecommunication decision tree. It will also be appreciated thatparameter-defining interface 700 can include an option to add anadditional path, remove a path, etc.

In some instances, one of the paths can be identified as a default path.Trajectories may then generally be routed to the default path unless(for example) a machine-learning model predicts that another path willhave at least a threshold degree of greater probability of resulting inthe target outcome, traversal through another path will produceadditional data for the path that is of a threshold value (e.g., asindicated by a predicted improvement in confidences of subsequentpredictions), etc. In some instances, whether a default path is selecteddepends on a confidence associated with a probability of the targetoutcome occurring (e.g., unless it is predicted that another path has atleast a 60% probability of resulting in a target outcome and that theprobability has a confidence of at least 50%).

In some instances, a switch icon can be configured to select a pathand/or next action (or lack thereof) and a time to extend the path to anext icon (e.g., and perform any next action). The time can be selectedfrom amongst multiple times and/or along an open or closed continuum. Inthe depicted instance, parameter-defining interface 700 includes amaximum time at which the trajectory is extended to a nextaction-defining icon. Thus, here, the trajectory is to be extended nolater than one day after the trajectory has reached the switch iconunless decision logic executed in association with the switch iconindicates that another time period is sufficiently more advantageous(e.g., due to a higher probability of resulting in a target outcomeand/or to increased training data).

A machine-learning technique and/or other selection technique can beconfigured to identify a path from amongst multiple potential paths thatis associated with a highest probability of resulting in a targetoutcome. In some instances, the technique further introduces some degreeof noise and/or variability such that a path deemed to be sub-optimalare occasionally selected to facilitate continue training of anunderlying model.

In some instances, a client may have a reason to introduce a biastowards or against selection of a particular path. For example, aparticular path may be costly (e.g., computationally and/or financially)to use relative to another path. As another example, a particular pathmay have high availability relative to another path. As yet anotherexample, a client may desire to quickly gain information as to anefficacy of a given path so as to inform subsequent resource-allocationdecisions.

Thus, parameter-defining interface 700 can include one or more optionsto effect a bias towards or against individual paths. In the depictedinstance, a slider is provided for each path. When the slider ispositioned towards the right “Boost” side, the path-selection techniquecan be adjusted to be biased towards a corresponding path. When theslider is positioned towards the left “Constrain” side, thepath-selection technique can be adjusted to be biased against acorresponding path. Such boosting and/or constraining options may haveimposed limits, such that (for example) an effect of moving the sliderto the left-most constrain position is not to prevent a selection of acorresponding path. Such limits can allow a machine-learning model tocontinue to collect data pertaining to various options and continue tomodify one or more parameters through learning. When there are only twooptions, a single interface component may be provided to identifyrelative bias towards one option versus the other. Meanwhile,option-specific boosting/constraining options can provide more intuitivecontrols when there are more than two options.

FIG. 8 shows another parameter-defining interface 800 that includesoptions to effect a bias towards or against representing various contentin communications. In the depicted instance, nine content items (eachrepresenting a corresponding product) are represented. A slider isprovided in visual association with a representation of each contentitem. When the slider is positioned towards the right “Boost” side, acontent selection (e.g., which can correspond to selecting betweenmultiple paths representing different content or can correspond toselecting content subsequent to identifying a communications channel)can be adjusted to be biased towards a corresponding content item. Whenthe slider is positioned towards the left “Constrain” side, thepath-selection technique can be adjusted to be biased against acorresponding item.

In the depicted instance, the slider is positioned to a left-mostposition. This triggers a “Never Offer” option to be presented. In someinstances, if the Never Offer option is not selected, the first contentitem may at least occasionally still be selected.

Based on the relative biases indicated by the sliders and historicalcommunication counts, a system can predict a number of times thatindividual content items will be represented in a given day. Thus, as aclient moves one or more sliders, interface 800 may automatically updateestimated counts as to a number of times that individual content itemswill be presented (e.g., per day) given the slider positions.

It will be appreciated that different types of biases can further beidentified and effected. For example, one or more sliders may beprovided to indicate biases related to when a communication istransmitted. For example, a slider may indicate an extent to which tobias decisions towards an immediate transmission (and/or towardstransmission at another time, such as at a capped time).

Effecting a bias (e.g., towards or against a type of communicationchannel, towards or against representing particular types of content,towards or against transmitting a communication at a particular time,etc.) can include (for example) modifying one or more weights in amachine-learning models and/or one or more thresholds. In someinstances, effecting a bias includes performing a post-processing (e.g.,to redistribute a portion of the results to improve an extent to which adistribution of communication attributes matches a target distributionindicated based on the bias(es).

FIG. 9 shows yet another parameter-defining interface 900 that includesoptions to effect a bias towards or against using various communicationchannels to transmit communications. In the depicted instance, threecommunication channels are represented: email, app-based notificationand SMS message. A slider is provided in visual association with arepresentation of each channel. When the slider is positioned towardsthe right “Boost” side, a content transmission can be adjusted to bebiased towards using the corresponding type of channel. When the slideris positioned towards the left “Constrain” side, the path-selectiontechnique can be adjusted to be biased against a corresponding channel.

Interface 900 further shows a time-series representation indicating anumber of communications that have been transmitted using each channelwithin a recent time period and further indicating a number ofcommunications scheduled for transmission using each channel. across anupcoming time period. A current time is represented by the verticalline. The communications can be scheduled in accordance with a selectiontechnique that selects between multiple potential transmission times(e.g., which may be included in a same or different machine-learningmodel relative to one selecting a communication channel). Thus, a clientcan view scheduled loads across various channels and determine whetherto adjust any biases set for or against a channel.

FIG. 10 shows a flowchart for a process 1000 for using machine learningto direct trajectories through a communication decision tree accordingto some embodiments of the invention. Process 1000 begins at block 1005where a data structure representing a communication decision tree isaccessed. The communication decision tree can be configured todynamically define individual trajectories through the communicationdecision tree using a machine-learning technique to indicate a series ofcommunication specifications. More specifically, the communicationdecision tree can include a set of nodes. A given trajectory can beextended across nodes in response to detecting an event indicating thatsuch extension is to occur. An event can include (for example) detectinga particular type of action or communication from a user event or caninclude identifying a particular decision (corresponding to a nodeidentification) at a trajectory-managing system or machine learning dataplatform. The set of nodes can include a set of branching nodes. Eachbranching node of the set of branching nodes can correspond to an actionpoint configured to identify a direction for a given trajectory and/orto identify a particular action to be initiated at a trajectory-managingsystem or machine learning data platform. A branching node can beconfigured to identify the direction or action using a configuredmachine learning model.

At block 1010, it is detected that a trajectory (associated with aparticular user and/or particular user device) has extended to reach abranching node of the communication decision tree. The particular usercan be one of a set of users included in a target group of communicationrecipients or target audience (e.g., each being associated with one ormore predefined attributes identified by a client). The target group ofcommunication recipients or target audience need not (though it may) bestatically defined. For example, it can represent a dynamic set thatcorresponds to profiles that—at various points in time—represent each ofone or more predefined attributes. The trajectory may have been extendedto the branching node as a result of detecting a particular type ofevent initiated at the user device (e.g., a communication indicatingthat the user device is engaged in a session at a client-associated website, a communication indicating that the user has completed a profileform submission, etc.) and/or as a result of completing a particularsystem-initiated action.

At block 1015, learned data that has been generated by processing otheruser data is retrieved. The other user data can correspond to dataassociated with at least part of the target group of communicationrecipients and/or target audience. The learned data can include datagenerated while training a machine-learning technique. It will beappreciated that the training may occur during a separate time relativeto using the machine-learning technique to direct one or moretrajectories, or the training and utilization of the machine-learningtechnique may be performed concurrently. The other user data can includetrajectory data associated with one or more trajectories through a sameor different communication decision tree. For example, the other userdata can indicate for which of the at least part of the target group ofcommunication recipients a corresponding trajectory reached a targetnode in a communication decision tree as specified in a predefinedtrajectory objective (e.g., indicating a success of a workflow). Atarget node may represent (for example) interacting with content, aconversion or responding to a communication. As another alternative oradditional example, the other user data can indicate for which of the atleast part of the target group a corresponding trajectory reached apreidentified node representing an undesired result (e.g., lack ofresponding to a communication, lack of a conversion or lack ofinteracting with content). The other user data can indicate profile dataand/or attributes corresponding to one or more users and can furtherindicate various events detected and/or initiated in association withindividual trajectories. Thus, for example, the other user data mayindicate a probability of detecting a particular type of event (e.g.,identified by a client as a target outcome) when various circumstancesexist.

At block 1020, one or more user attributes associated with a usercorresponding to the trajectory (detected as extending to the branchingnode) are retrieved. The user attribute(s) can include (for example) atype of user device; a geographical location of a user device; a type ofbrowser being used at the user device; an operating system being used atthe user device; a partial or complete history of an interaction betweenthe user device and a particular web site; an interaction between theuser device and one or more other web sites, cookie data associated withthe user device; historical data indicating types of notifications(e.g., types of emails, text messages and/or app messages) that wereopened at the user device, that resulted in activation of an includedlink, etc. The one or more particular user attributes can be collectedand/or retrieved locally and/or requested and received from a remotesource.

At block 1025, one or more communication specifications are identifiedbased on the learned data and the one or more user attributes. Forexample, the learned data can include one or more parameters of amachine-learning model (e.g., a regression model). The machine-learningmodel may further be defined based on one or more hyperparameters. Themachine-learning model can then be configured to process the userattribute(s) using the parameter(s), hyperparameter(s) and/or underlyingstructure. A result of an implementation of the model may identify aselection from amongst multiple available options that is predicted tobe the most successful in achieving a target outcome. The multipleavailable options may correspond to (for example) different types ofcommunication channels to be used, different types of content to betransmitted, and/or different timings of transmission. In someinstances, the multiple available options share one or more othercommunication specifications.

At block 1030, transmission of content to a user device associated withthe trajectory is triggered. The content transmission is performed inaccordance with the one or more communication specifications.

At block 1035, it is determined whether the trajectory has extended toreach another branching node within the communication decision tree. Thedetermination can include determining (for example) whether a thresholdamount of time has passed since a last communication was transmitted tothe particular user (or corresponding device); that the particular userinteracted with a last communication transmitted to the particular user(or corresponding device) and/or that the particular user interactedwith target content irrespective of whether such interaction with thetarget content was a result of a last communication transmitted to theparticular user (or corresponding device). In some instances, each oftwo or more of these determinations is associated with a criterion of adifferent branching node. Block 1035 can include identify which otherbranching node to which the trajectory has extended.

If it is determined that the trajectory has extended to reach anotherbranching node, process 1000 returns to block 1015 and blocks 1015-1035are repeated. However, the repeated iteration of block 1015 may includeretrieving different learned data generated by processing other userdata (e.g., potentially, but not necessarily, in combination with atleast some of the user data). The different learned data may have beengenerated using a same or different configuration of themachine-learning technique (e.g., having same or different values and/ortypes of parameters and/or hyperparameters). The repeated iteration ofblock 1020 can include retrieving at least one other user attribute. Therepeated iteration of block 1025 can include identifying at least oneother communication specification (and/or from amongst a different setof potential communication specifications) based on the differentlearned data and the at least one other user attribute. The at least oneother communication specification can be identified using a same ordifferent type of underlying model. And the repeated iteration of block1030 can include triggering another transmission of other content inaccordance with the at least one other communication specification.

When it is determined that the trajectory has not extended to reachanother branching node, process 1000 proceeds to block 1040 to determinewhether the trajectory is complete. The determination can be made bydetermining whether a current end of a trajectory is associated with atrajectory that lacks an extending connection. If it is determined thatthe trajectory is complete, processing of the trajectory can beterminated. If it is determined that the trajectory is not complete,process 1000 can return to block 1035 to await a determination that thetrajectory has reached another branching node (e.g., as a result of auser-initiated action or external event).

Thus, process 1000 facilitates repeatedly using differently configuredmachine-learning models to identify specifications corresponding todifferent stages in a communication exchange. At the different stages,the models can use different profile data (e.g., values for differentfields or values that have changed in time) and/or different modelparameters (e.g., learned based on different inputs and/or outputspertaining to the models and/or based on temporal changes). Thisiterative application of machine-learning models facilitates dynamicallydirecting communication exchanges for individual users.

FIG. 11 shows a flowchart for a process 1100 for defining amachine-learning-based communication decision tree using an interfacesupporting positionable visual elements. Process 1100 begins at block1105 where an interface is availed that includes a set of visualelements and a canvas for element positioning. Each of the set of visualelements can be positionable on the canvas. For example, the interfacemay be configured to allow a user to click on a representation of avisual element and—while maintaining the click—drag a cursor to anotherposition on the canvas to drop the visual element at the other position.As another example, a representation can be selected (e.g., via a clickor double-click) and another input (e.g., another click or double-click)received while the cursor is at another position can cause the visualelement to be positioned at the other position.

The set of visual elements can include a set of action-defining visualelements. Each action-defining visual element of the set ofaction-defining visual elements can a particular action that is to beperformed when a given trajectory has extended to the action-definingvisual element. The set of action-defining visual elements can include aswitch visual element that represents a decision action (e.g., madeusing a machine-learning model) to identify a communicationspecification using a machine-learning technique. The set ofaction-defining visual elements can further include a set ofcommunication visual elements. Each of the set of communication visualelements can represent a particular communication specification (e.g., atype of communication channel, specific content, transmission time,etc.). The set of visual elements can also include a connection visualelement configured to directionally connect multiple positioned visualelements. Each positioned visual element of the multiple positionedvisual elements can correspond to an action-defining visual element ofthe set of action-defining visual elements. The directional connectioncan indicate an order in which particular actions represented by themultiple positioned visual elements are to occur.

At block 1110, an update to the canvas is detected. The updated canvascan include the switch visual element being positioned at a firstposition within the canvas, a first communication visual element of theset of communication visual elements positioned at a second positionwithin the canvas, and a second communication visual element of the setof communication visual elements being positioned a third positionwithin the canvas. The first communication visual element can representa first particular communication specification, and the secondcommunication visual element can represent a a second particularcommunication specification.

The updated canvas can further include a set of connection visualelements. Each of the set of connection visual elements can include aninstance of the connection visual element. A first connection of the setof connection visual elements can be positioned to connect the switchvisual element to the first communication visual element. A secondconnection of the set of connection visual elements can be positioned toconnect the switch visual element to the second communication visualelement. The set of connection visual elements can indicate thatpotential results of execution of the machine-learning technique at theswitch visual element include a first result that triggers acommunication transmission having the first particular communicationspecification and a second result that triggers a communicationtransmission having the second particular communication specification.

At block 1115, a particular communication decision tree is defined basedon the updated canvas. At block 1120, it is detected that a giventrajectory associated with particular profile data has extended to aparticular decision action represented by the switch visual element. Inresponse to the detection, at block 1125, the machine-learning technique(configured with learned parameter data and/or static data) is used toprocess the particular profile data to produce a machine-learningresult. The learned parameter data can include data learned during aseparate or ongoing training of a machine-learning model based on a setof trajectories associated with other users and/or associated with asame or different communication decision tree. The processing of theparticular profile data using the machine-learning technique canindicate which one of the first and second particular communicationspecifications is to be applied for a content transmission.

Thus, at block 1130, content is transmitted to a user device associatedwith the trajectory. The transmission is performed in accordance withthe one of the first and second particular communication specificationsas indicated in the machine-learning result. For example, the first andsecond communication visual elements may correspond to different typesof communication channels. Block 1125 may then include identifying oneof the two types of communication channels, and the content can betransmitted via the identified channel.

Thus, the canvas facilitates defining configurations for a communicationdecision tree. However, a client need not define a communicationexchange that applies to all users and/or that includes merely one ormore deterministic rules. Rather, the interface supports generallyidentifying options of various communication specifications, an order ofcommunication events and/or constraints. Specification communicationspecifications can then be automatically and dynamically generated usingmachine-learning techniques. This approach can facilitate configuring acommunication system to abide by client priorities but can allow thecommunication system to dynamically adapt to characteristics ofparticular users, resource loads, recent interaction patterns, etc.

It will be appreciated that variations of the disclosed techniques arecontemplated. For example, a branching node may use another type ofartificial-intelligence model that is not a machine-learning model toselect a communication specification to be used for a communication. Asanother example, an interface may be configured to accept a selection ofa particular type or a more general type of artificial-intelligencemodel that is to be used at a trajectory stage corresponding to a switchelement. As yet another example, an interface may be configured to allowan indication of what data (e.g., in terms of corresponding to one ormore communication decision trees, one or more time periods, and/or oneor more user-population segments) is to be used to train amachine-learning model corresponding to one, more or all switch elementspositioned on a canvas.

It will be appreciated that technology disclosed herein can be used tosupport various types of decision trees. For example, nodes in the treeand/or visual elements represented on a canvas can (in some instances)correspond to elements that generally are associated with logic thatevaluates whether a given condition is satisfied (e.g., a particulartype of inter-device communication is detected, a non-client-associatedapplication indicates that an action was performed, a particular timehas passed) and, upon detecting satisfaction, a particular action isperformed. For a subset of the nodes and/or visual elements, theconditioned particular action can include executing a machine-learningmodel based on profile data to select from amongst a set of connectednodes (or visual elements) to proceed, such that another particularaction associated with the selected node (or visual element) can beperformed. For example, machine-learning-based selection of trajectorypaths may be integrated into an If This Then That environment. Ratherthan having branching nodes connected to nodes identifying communicationspecifications, the branches could (for example) identify differentapplications to use to store data. Thus, a decision framework can beestablished to enable an artificial-intelligence applet and/or plugin tocommunicate with one or more other applets or back through a canvas.

It will further be appreciated that, while some disclosures hereinindicate that a target outcome can be used to shape machine-learningtraining and execution, more complicated instances are considered. Forexample, a negative outcome (e.g., an unsubscribe request or complaint)can alternatively or additionally be identified and used. In someinstances, a score can be assigned to various results based on aquantity or extent to which one or more target results and/or one ormore negative results occurred. The score can then be used for trainingand implementing one or more machine-learning models.

Specific details are given in the above description to provide athorough understanding of the embodiments. However, it is understoodthat the embodiments can be practiced without these specific details.For example, circuits can be shown in block diagrams in order not toobscure the embodiments in unnecessary detail. In other instances,well-known circuits, processes, algorithms, structures, and techniquescan be shown without unnecessary detail in order to avoid obscuring theembodiments.

Implementation of the techniques, blocks, steps and means describedabove can be done in various ways. For example, these techniques,blocks, steps and means can be implemented in hardware, software, or acombination thereof. For a hardware implementation, the processing unitscan be implemented within one or more application specific integratedcircuits (ASICs), digital signal processors (DSPs), digital signalprocessing devices (DSPDs), programmable logic devices (PLDs), fieldprogrammable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described above, and/or a combination thereof.

Also, it is noted that the embodiments can be described as a processwhich is depicted as a flowchart, a flow diagram, a data flow diagram, astructure diagram, or a block diagram. Although a flowchart can describethe operations as a sequential process, many of the operations can beperformed in parallel or concurrently. In addition, the order of theoperations can be re-arranged. A process is terminated when itsoperations are completed, but could have additional steps not includedin the figure. A process can correspond to a method, a function, aprocedure, a subroutine, a subprogram, etc. When a process correspondsto a function, its termination corresponds to a return of the functionto the calling function or the main function.

Furthermore, embodiments can be implemented by hardware, software,scripting languages, firmware, middleware, microcode, hardwaredescription languages, and/or any combination thereof. When implementedin software, firmware, middleware, scripting language, and/or microcode,the program code or code segments to perform the necessary tasks can bestored in a machine readable medium such as a storage medium. A codesegment or machine-executable instruction can represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, asoftware package, a script, a class, or any combination of instructions,data structures, and/or program statements. A code segment can becoupled to another code segment or a hardware circuit by passing and/orreceiving information, data, arguments, parameters, and/or memorycontents. Information, arguments, parameters, data, etc. can be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, ticket passing, network transmission, etc.

For a firmware and/or software implementation, the methodologies can beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine-readable mediumtangibly embodying instructions can be used in implementing themethodologies described herein. For example, software codes can bestored in a memory. Memory can be implemented within the processor orexternal to the processor. As used herein the term “memory” refers toany type of long term, short term, volatile, nonvolatile, or otherstorage medium and is not to be limited to any particular type of memoryor number of memories, or type of media upon which memory is stored.

Moreover, as disclosed herein, the term “storage medium” can representone or more memories for storing data, including read only memory (ROM),random access memory (RAM), magnetic RAM, core memory, magnetic diskstorage mediums, optical storage mediums, flash memory devices and/orother machine readable mediums for storing information. The term“machine-readable medium” includes, but is not limited to portable orfixed storage devices, optical storage devices, wireless channels,and/or various other storage mediums capable of storing that contain orcarry instruction(s) and/or data.

While the principles of the disclosure have been described above inconnection with specific apparatuses and methods, it is to be clearlyunderstood that this description is made only by way of example and notas limitation on the scope of the disclosure.

What is claimed is:
 1. A computer-implemented method comprising:accessing a data structure representing a communication decision treeconfigured to dynamically define individual trajectories through thecommunication decision tree using a machine-learning technique toindicate a series of communication specifications, the communicationdecision tree including a set of branching nodes, each branching node ofthe set of branching nodes corresponding to an action point configuredto identify a direction for a given trajectory; detecting, at a firsttime, that a trajectory through the communication decision tree hasreached a first branching node of the set of branching nodes, thetrajectory being associated with a particular user; in response to thedetecting that the trajectory has reached the first branching node:retrieving first learned data generated by processing first user datausing a machine-learning technique, the first user data including userattributes for a set of other users; retrieving one or more particularuser attributes associated with the particular user; identifying one ormore first communication specifications based on the first learned dataand the one or more particular user attributes; and causing firstcontent to be transmitted to a user device associated with theparticular user in accordance with the one or more first communicationspecifications; detecting, at a second time that is after the firsttime, that the trajectory through the communication decision tree hasreached a second branching node of the set of branching nodes; and inresponse to the detecting that the trajectory has reached the secondbranching node: retrieving second learned data generated by processingsecond user data using the machine-learning technique, the second userdata including at least some user attributes not included in the firstuser data; identifying one or more second communication specificationsbased on the second learned data and at least some of the one or moreparticular user attributes; and causing second content to be transmittedto the user device in accordance with the one or more secondcommunication specifications.
 2. The method of claim 1, wherein thefirst learned data includes one or more parameters corresponding to aregression model, and wherein the second learned data includes one ormore other parameters corresponding to a same or different regressionmodel.
 3. The method of claim 1, wherein: processing the first user datausing the machine-learning technique includes generating an outputindicative of a probability that the trajectory will reach a particularaction node represented in the communication decision tree when aparticular communication technique is implemented; determining, based onthe output and one or more trajectory-routing constraints, whether toimplement the particular communication technique with respect to thetrajectory, the one or more first communication specifications beingidentified based on the determination.
 4. The method of claim 1, whereinidentifying the one or more first communication specifications includesselection a particular type of communication channel from amongst a setof types of communication channels, and wherein causing the firstcontent to be transmitted in accordance with the one or more firstcommunication specifications includes causing the first content to betransmitted across a communication channel of the particular type ofcommunication channel.
 5. The method of claim 1, wherein identifying theone or more first communication specifications includes identifying atime within a time range, and wherein causing the first content to betransmitted in accordance with the one or more first communicationspecifications includes causing the first content to be transmitted atthe identified time.
 6. The method of claim 1, wherein the detectingthat the trajectory through the communication decision tree has reachedthe first branching node includes detecting that: a communication thatincludes an address or number associated with the particular user hasbeen received from the user device; or an email previously transmittedto the address associated with the particular user has been opened. 7.The method of claim 1, wherein the detecting that the trajectory throughthe communication decision tree has reached the second branching nodeincludes detecting that a webpage associated with a particular domainhas been requested by the user device.
 8. The method of claim 1, whereineach of first user data and the second user data includes anonymized orpartially anonymized data.
 9. The method of claim 1, further comprising,in response to detecting that the trajectory has reached the secondbranching node: identifying at least one new user attribute associatedwith the particular user, wherein the one or more second communicationspecifications are identified further based on the at least one new userattribute.
 10. The method of claim 1, wherein a target group ofcommunication recipients includes the particular user, and wherein themachine-learning technique is configured to perform training based onextents to which trajectories corresponding to at least part of thetarget group of communication recipients satisfied one or morepredefined trajectory objectives.
 11. The method of claim 1, whereindetecting that the trajectory has reached the second branching nodeincludes: detecting that a threshold amount of time has passed since alast communication to the particular user; detecting that the particularuser interacted with a last communication to the particular user; and/ordetecting that the particular user interacted with target contentirrespective of whether such interaction with the target content was aresult of a last communication to the particular user.
 12. Acomputer-program product tangibly embodied in a non-transitorymachine-readable storage medium, including instructions configured tocause one or more data processors to perform actions including:accessing a data structure representing a communication decision treeconfigured to dynamically define individual trajectories through thecommunication decision tree using a machine-learning technique toindicate a series of communication specifications, the communicationdecision tree including a set of branching nodes, each branching node ofthe set of branching nodes corresponding to an action point configuredto identify a direction for a given trajectory; detecting, at a firsttime, that a trajectory through the communication decision tree hasreached a first branching node of the set of branching nodes, thetrajectory being associated with a particular user; in response to thedetecting that the trajectory has reached the first branching node:retrieving first learned data generated by processing first user datausing a machine-learning technique, the first user data including userattributes for a set of other users; retrieving one or more particularuser attributes associated with the particular user; identifying one ormore first communication specifications based on the first learned dataand the particular user attributes; and causing first content to betransmitted to a user device associated with the particular user inaccordance with the one or more first communication specifications;detecting, at a second time that is after the first time, that thetrajectory through the communication decision tree has reached a secondbranching node of the set of branching nodes; and in response to thedetecting that the trajectory has reached the second branching node:retrieving second learned data generated by processing second user datausing the machine-learning technique, the second user data including atleast some user attributes not included in the first user data;identifying one or more second communication specifications based on thesecond learned data and at least some of the one or more particular userattributes; and causing second content to be transmitted to the userdevice in accordance with the one or more second communicationspecifications.
 13. The computer-program product of claim 12, whereinthe first learned data includes one or more parameters corresponding toa regression model, and wherein the second learned data includes one ormore other parameters corresponding to a same or different regressionmodel.
 14. The computer-program product of claim 12, wherein: processingthe first user data using the machine-learning technique includesgenerating an output indicative of a probability that the trajectorywill reach a particular action node represented in the communicationdecision tree when a particular communication technique is implemented;determining, based on the output and one or more trajectory-routingconstraints, whether to implement the particular communication techniquewith respect to the trajectory, the one or more first communicationspecifications being identified based on the determination.
 15. Thecomputer-program product of claim 12, wherein identifying the one ormore first communication specifications includes selecting a particulartype of communication channel from amongst a set of types ofcommunication channels, and wherein causing the first content to betransmitted in accordance with the one or more first communicationspecifications includes causing the first content to be transmittedacross a communication channel of the particular type of communicationchannel.
 16. The computer-program product of claim 12, whereinidentifying the one or more first communication specifications includesidentifying a time within a time range, and wherein causing the firstcontent to be transmitted in accordance with the one or more firstcommunication specifications includes causing the first content to betransmitted at the identified time.
 17. The computer-program product ofclaim 12, wherein the detecting that the trajectory through thecommunication decision tree has reached the first branching nodeincludes detecting that: a communication that includes an address ornumber associated with the particular user has been received from theuser device; or an email previously transmitted to the addressassociated with the particular user has been opened.
 18. Thecomputer-program product of claim 12, wherein the detecting that thetrajectory through the communication decision tree has reached thesecond branching node includes detecting that a webpage associated witha particular domain has been requested by the user device.
 19. A systemcomprising: one or more data processors; and a non-transitory computerreadable storage medium containing instructions which when executed onthe one or more data processors, cause the one or more data processorsto perform actions including: accessing a data structure representing acommunication decision tree configured to dynamically define individualtrajectories through the communication decision tree using amachine-learning technique to indicate a series of communicationspecifications, the communication decision tree including a set ofbranching nodes, each branching node of the set of branching nodescorresponding to an action point configured to identify a direction fora given trajectory; detecting, at a first time, that a trajectorythrough the communication decision tree has reached a first branchingnode of the set of branching nodes, the trajectory being associated witha particular user; in response to the detecting that the trajectory hasreached the first branching node: retrieving first learned datagenerated by processing first user data using a machine-learningtechnique, the first user data including user attributes for a set ofother users; retrieving one or more particular user attributesassociated with the particular user; identifying one or more firstcommunication specifications based on the first learned data and theparticular user attributes; and causing first content to be transmittedto a user device associated with the particular user in accordance withthe one or more first communication specifications; detecting, at asecond time that is after the first time, that the trajectory throughthe communication decision tree has reached a second branching node ofthe set of branching nodes; and in response to the detecting that thetrajectory has reached the second branching node: retrieving secondlearned data generated by processing second user data using themachine-learning technique, the second user data including at least someuser attributes not included in the first user data; identifying one ormore second communication specifications based on the second learneddata and at least some of the one or more particular user attributes;and causing second content to be transmitted to the user device inaccordance with the one or more second communication specifications. 20.The system of claim 19, wherein the first learned data includes one ormore parameters corresponding to a regression model, and wherein thesecond learned data includes one or more other parameters correspondingto a same or different regression model.